Automated tracking in digitized videofluoroscopy sequences for spine kinematic analysis

Authors:
Shing Chun Benny Lam;Brendan McCane;Robert Allen
Affiliations:
Institute of Sound and Vibration Research, University of Southampton, Southampton SO17 1BJ, UK;Department of Computer Science, University of Otago, Dunedin, New Zealand;Institute of Sound and Vibration Research, University of Southampton, Southampton SO17 1BJ, UK
Venue:
Image and Vision Computing
Year:
2009

Citing 10
Cited 0

Computer graphics

Computer graphics
Digital image processing (2nd ed.)

Digital image processing (2nd ed.)
CONDENSATION—Conditional Density Propagation forVisual Tracking

International Journal of Computer Vision
A Probabilistic Exclusion Principle for Tracking Multiple Objects

International Journal of Computer Vision
Joint Probabilistic Techniques for Tracking Multi-Part Objects

CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
A Particle Filter to Track Multiple Objects

WOMOT '01 Proceedings of the IEEE Workshop on Multi-Object Tracking (WOMOT'01)
MCMC-Based Particle Filtering for Tracking a Variable Number of Interacting Targets

IEEE Transactions on Pattern Analysis and Machine Intelligence
Multi-object segmentation using shape particles

IPMI'05 Proceedings of the 19th international conference on Information Processing in Medical Imaging
A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking

IEEE Transactions on Signal Processing
Visual tracking and recognition using appearance-adaptive models in particle filters

IEEE Transactions on Image Processing

Quantified Score

Hi-index	0.00

Visualization

Abstract

Spine kinematic analysis provides useful information to aid understanding of the segmental motion of the vertebrae. Digitized videofluoroscopy (DVF) is the existing practical modality to image spine motion for kinematic data acquisition. However, obtaining kinematic parameters from DVF sequence requires manual landmarking which is a laborious process and can be subjective and error prone. This work develops an automated spine motion tracking algorithm for DVF sequences within a Bayesian framework. By utilizing the anatomical relationships between vertebrae, a dynamic Bayesian network with a particle filter at each node is constructed. The proposed algorithm overcomes the dimensionality problem in a regular particle filter and has more efficient and robust performance. It can provide results of about 1^o and 2pixels (0.2mm) variability in rotation and translation estimation, respectively, during repeated initialization analysis on sequences from simulation and in vivo healthy human subject studies.